1. Field of the Invention
The present invention relates to systems and methods for the manipulation, presentation, and display of information. More particularly, the present invention relates to computer-implemented systems and methods which aggregate and prioritize data from any size data universe.
2. Description of Related Art
In the past twenty years there has been an explosion in the amount of information, and, more particularly, computer-accessible information made available to professionals in all fields. Especially in the medical field, key among the reasons for this explosion are more sophisticated and accurate testing techniques. Such techniques produce numerical and non-numerical measurements quantifying multiple parameters. Just a few years ago, many of the measurements now provided to professionals were unavailable. Because these measurements are easily transmitted using computer-based communication, there has been a tremendous increase in the amount of information provided to professionals in many fields of endeavor.
An unfortunate consequence of making large amounts of information available to professionals is that the finite capacity of human beings to absorb and to contextualize information may be exceeded, which may lead to adverse consequences. Important data may become buried in unimportant data. Some important data may even be inadvertently ignored. Signals of changes in data reflecting trends in the measurement of important parameters are missed. Valuable time is spent reviewing inconsequential background data. Missing key data or changes in data may vitally delay understanding the reasons for data distortions or data anomalies in key indicators. An additional problem is that often the software distributes the relevant data to many screens. A review requires many steps to examine the data. Another problem is that for many uses, the software system is defined and “read only” for the user. Any special concerns the user may have are not addressed by the software system. Yet another problem is that many reports are language-based rather than number-based, and automated systems do not identify critical results in text documents. A further problem is that often the user wants the data divided into two categories, namely “what I need to know now” and “what is not important to me”. One user may find critical what another would not. A surgeon, for example, may want to see the data differently from an internist. Finally, simplicity of display decreases user fatigue. Currently, systems display a constant and significant amount of information as text, much of which may be of little or no interest to the user.
One example illustrating the problem of exceeding the finite capacity of human beings to absorb and contextualize information is in the treatment of patients by health care providers. Health care providers are given complex patient reports to read, analyze, and then, based on their analysis of the data presented, prescribe a course of patient treatment or therapy. Such a report on a particular patient may include personal information, test results, diagnoses, symptoms, and analyses on multiple pages in an arrangement that varies from patient to patient.
Current methods to identify critical results include identifying them by highlighting in red. Some systems place the critical reports at the top of the stack of reports for review. The reports are always presented as text. One conventional display of stacked test reports shows critical results highlighted in pink and abnormal, but not critical, results highlighted in yellow. A busy practitioner may be called upon to review many such arrays of medical information each day. A practitioner may also receive reports in different formats, including, but not limited to, electronically, on paper, or by facsimile. The location of the data categories varies from one system to another. Despite the effort spent reviewing each chart, the complex nature of a medical chart in combination with physician fatigue or time limitations may cause key information, such as a critical variance from a norm or a data anomaly, to be missed. The consequences of missing key items of data can be extremely dangerous or even fatal.
In addition to spotting a data variance from a norm, the professional must also be able to have a context for the data, which may require additional information from the particular patient's medical record to be able to prioritize the information to determine what may be the most critical of the variances from a norm and what, as opposed to other variances from a norm, may be less critical. Further, it would be advantageous to provide information regarding generally accepted interventions or therapy needed to respond to one or more critical variances from a norm.
Once a practitioner reviews a given report or set of reports, there may be a need for drug therapy intervention. If the patient has a unique condition or if the attending physician is not familiar with the use of some medications, the attending physician may have to consult a reference to select the correct medication. A critical variance from a norm or an anomaly in reported data must be identified, and a generally-accepted drug therapy intervention, if required, must be selected. The medical doctor must also be aware of any potential adverse consequences from recommended interventions, such as negative drug interactions.
In many situations, the aggregation of multiple sets of data, for example, from multiple patients, enables the discovery of characteristics of trends found in a larger universe of information revealed by observing multiple sets of data.